With medical care expenses compounding every year, effective population health risk management is essential. Risk stratification systems, for computer assisted clinical decision support, are necessary for determining risks of patient populations in regard to certain conditions and facilitating health management. But present systems for managing population health risks do not harness valuable electronic health record data and claims experience for categorizing patient risks. As a consequence, inaccurate and imprecise risk assignment often results, rendering these systems less effective at understanding their population of patients. This contributes to decreased quality of care, increased risk of medical errors, and increased cost of healthcare. Additionally, specific knowledge of patient risk strata can enable health care administrators to develop wellness programs with population-specific conditions in mind, more accurately forecast future spend levels, and anticipate resource needs. It is of great significance, then, to improve upon conventional technological approaches to achieve a greater degree of accuracy and dependability, especially as applied to a target individual as opposed to a population as a whole—a drawback that conventional approaches have not been able to effectively overcome.